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Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

We have compiled an inventory of 1004 rock glaciers for Uttarakhand State, India, using high‐resolution satellite data freely available on Google Earth. The inventory is used to analyze the origin, spatial distribution, geometry and dynamics of rock glaciers using a combination of optical remote sensing techniques with a geographic information system (GIS). Results show that development of rock glaciers in this region depends strongly on high elevation (> 4000 m a.s.l.) and slope aspect. Rock glaciers are more dominant towards the southern quadrant (S, SE, SW) than the northern quadrant (N, NE, NW). A large number (n = 608) of small (<0.5 km2) rock glaciers originating from glacial moraine indicates glacial retreat in this region as one of the major causes for the formation of such a large number of rock glaciers. Median elevation of intact rock glaciers indicates that climatic conditions above 4600 m a.s.l. are suitable for the existence of permafrost in this region and that the lower limit of discontinuous permafrost gradually increases from west to east. Despite mean annual air temperatures below 0°C, increasing mean temperatures during warmest quarter of the year could be a strong controlling factor for permafrost thawing in the region. Logistic regression modeling using WorldClim version 2 climate data sets and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data show that these models can produce fairly reliable estimates of permafrost probability in the studied area. MODIS LST climate data sets can be crucial for mapping and monitoring permafrost in the region.

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